Data Intelligence Solution Overview

Unlock the Full Value
Of Simulation Data

From Scattered Data to Unified to Actionable Insights

Most simulation data ends up scattered across file shares while CAD, CAE, PLM, and test systems operate in silos. Engineers waste hours searching for past work, compliance teams manually assemble audit evidence, and AI initiatives stall because data isn’t properly structured. Rescale automatically captures context at the point of simulation execution and unifies simulation results into a searchable, AI-ready knowledge base. Rescale Assistant provides natural language querying with full traceability to source data, while automations capture metadata to build datasets that power AI physics methods and agentic engineering workflows.

Search, analyze, and reason over your entire simulation history.


Business Impact

Data intelligence accelerates engineering productivity while unlocking strategic AI capabilities and reducing operational overhead.

  • 20-60% increase in engineering productivity by eliminating time spent hunting for data, re-running old studies, and manually compiling evidence for reviews
  • 40% reduction in manual data management work where automated metadata capture replaces manual report preparation and data compilation
  • 2x faster time to market and millions in R&D program value through better decisions, eliminated rework, and compressed development cycles

The business outcome of effective data intelligence deployment is unlocking AI initiatives. AI physics and agentic engineering require governed, well-structured datasets. Data intelligence transforms data exhaust into AI-ready assets and persistent engineering knowledge graphs.

Why Rescale

Rescale provides an R&D-specific data fabric and intelligence layer purpose-built for engineering workflows.

  • Capture Context at Source: Automatically preserves simulation metadata, configurations, and key findings as they’re generated, maintaining engineering context without manual documentation or custom integration work.
  • Data Fabric with Embedded AI: Pre-built data connectors and conversational AI search capabilities that deliver value immediately, avoiding the cost and complexity of custom tools.
  • Cross-System Integration: PLM and SPDM remain systems of record while data intelligence provides the connective fabric that makes information discoverable and AI-ready across silos.
  • Built for New AI Methods: Automatic organization of simulation data for model training pipelines, with integration into AI physics workflows and agent orchestration from day one.
Natural language queries trace simulation history, results, and requirements.

Use Cases

  • Digital Thread and Traceability: Link simulation data to product context, requirements, configurations, and engineering decisions across systems, enabling teams to understand design provenance while satisfying audit requirements without manual evidence assembly.
  • Engineering Knowledge Base: Provide semantic search and conversational access to historical reports and results with citations to source data, allowing engineers to leverage institutional knowledge rather than starting from scratch.
  • Compliance and Certification Automation: Continuously capture lineage and assemble evidence for regulatory reviews, reducing weeks of manual preparation to automated reports.
  • AI and ML Data Foundations: Automatically structure and version simulation data for model training, feeding AI physics surrogate development and enabling machine learning initiatives.
  • Supplier Data Integration: Ingest, normalize, validate, and integrate external simulation data into the digital thread, ensuring partner work becomes part of your engineering knowledge base.

Who Benefits

Learn More

Discover how capturing simulation context automatically and applying AI intelligence to data can unlock new insights and capabilities for engineering teams. Learn more.

×